Overview

Dataset statistics

Number of variables22
Number of observations2126
Missing cells0
Missing cells (%)0.0%
Duplicate rows13
Duplicate rows (%)0.6%
Total size in memory365.5 KiB
Average record size in memory176.1 B

Variable types

Numeric19
Categorical3

Warnings

Dataset has 13 (0.6%) duplicate rows Duplicates
histogram_mode is highly correlated with histogram_medianHigh correlation
histogram_mean is highly correlated with histogram_medianHigh correlation
histogram_median is highly correlated with histogram_mode and 1 other fieldsHigh correlation
accelerations has 894 (42.1%) zeros Zeros
fetal_movement has 1311 (61.7%) zeros Zeros
uterine_contractions has 332 (15.6%) zeros Zeros
light_decelerations has 1231 (57.9%) zeros Zeros
prolongued_decelerations has 1948 (91.6%) zeros Zeros
percentage_of_time_with_abnormal_long_term_variability has 1240 (58.3%) zeros Zeros
mean_value_of_long_term_variability has 137 (6.4%) zeros Zeros
histogram_number_of_peaks has 107 (5.0%) zeros Zeros
histogram_number_of_zeroes has 1624 (76.4%) zeros Zeros
histogram_variance has 187 (8.8%) zeros Zeros

Reproduction

Analysis started2021-01-27 18:44:36.463590
Analysis finished2021-01-27 18:45:42.000947
Duration1 minute and 5.54 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

baseline value
Real number (ℝ≥0)

Distinct48
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.303857
Minimum106
Maximum160
Zeros0
Zeros (%)0.0%
Memory size16.7 KiB
2021-01-28T00:15:42.114972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum106
5-th percentile119
Q1126
median133
Q3140
95-th percentile149
Maximum160
Range54
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.840844258
Coefficient of variation (CV)0.07382265209
Kurtosis-0.2929429111
Mean133.303857
Median Absolute Deviation (MAD)7
Skewness0.02031218895
Sum283404
Variance96.8422157
MonotocityNot monotonic
2021-01-28T00:15:42.292022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
133136
 
6.4%
130111
 
5.2%
122109
 
5.1%
138103
 
4.8%
12591
 
4.3%
12885
 
4.0%
12078
 
3.7%
14477
 
3.6%
14277
 
3.6%
13276
 
3.6%
Other values (38)1183
55.6%
ValueCountFrequency (%)
1067
 
0.3%
11021
1.0%
11216
0.8%
11411
 
0.5%
11528
1.3%
ValueCountFrequency (%)
1601
 
< 0.1%
15912
0.6%
15810
0.5%
1574
 
0.2%
1564
 
0.2%

accelerations
Real number (ℝ≥0)

ZEROS

Distinct20
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00317826905
Minimum0
Maximum0.019
Zeros894
Zeros (%)42.1%
Memory size16.7 KiB
2021-01-28T00:15:42.451057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.002
Q30.006
95-th percentile0.011
Maximum0.019
Range0.019
Interquartile range (IQR)0.006

Descriptive statistics

Standard deviation0.003865590954
Coefficient of variation (CV)1.216256677
Kurtosis0.7676482604
Mean0.00317826905
Median Absolute Deviation (MAD)0.002
Skewness1.204392079
Sum6.757
Variance1.494279343 × 105
MonotocityNot monotonic
2021-01-28T00:15:42.580086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0894
42.1%
0.003161
 
7.6%
0.002160
 
7.5%
0.001143
 
6.7%
0.004117
 
5.5%
0.006112
 
5.3%
0.005110
 
5.2%
0.008103
 
4.8%
0.00791
 
4.3%
0.00960
 
2.8%
Other values (10)175
 
8.2%
ValueCountFrequency (%)
0894
42.1%
0.001143
 
6.7%
0.002160
 
7.5%
0.003161
 
7.6%
0.004117
 
5.5%
ValueCountFrequency (%)
0.0191
 
< 0.1%
0.0182
 
0.1%
0.0174
0.2%
0.0167
0.3%
0.0159
0.4%

fetal_movement
Real number (ℝ≥0)

ZEROS

Distinct102
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.009480714958
Minimum0
Maximum0.481
Zeros1311
Zeros (%)61.7%
Memory size16.7 KiB
2021-01-28T00:15:42.740122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.003
95-th percentile0.028
Maximum0.481
Range0.481
Interquartile range (IQR)0.003

Descriptive statistics

Standard deviation0.04666584447
Coefficient of variation (CV)4.922186214
Kurtosis64.26082063
Mean0.009480714958
Median Absolute Deviation (MAD)0
Skewness7.811477236
Sum20.156
Variance0.00217770104
MonotocityNot monotonic
2021-01-28T00:15:42.920163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01311
61.7%
0.001164
 
7.7%
0.002112
 
5.3%
0.00388
 
4.1%
0.00449
 
2.3%
0.00536
 
1.7%
0.00631
 
1.5%
0.00728
 
1.3%
0.0125
 
1.2%
0.00925
 
1.2%
Other values (92)257
 
12.1%
ValueCountFrequency (%)
01311
61.7%
0.001164
 
7.7%
0.002112
 
5.3%
0.00388
 
4.1%
0.00449
 
2.3%
ValueCountFrequency (%)
0.4811
< 0.1%
0.4771
< 0.1%
0.471
< 0.1%
0.4691
< 0.1%
0.4551
< 0.1%

uterine_contractions
Real number (ℝ≥0)

ZEROS

Distinct16
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004366415804
Minimum0
Maximum0.015
Zeros332
Zeros (%)15.6%
Memory size16.7 KiB
2021-01-28T00:15:43.075198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.002
median0.004
Q30.007
95-th percentile0.009
Maximum0.015
Range0.015
Interquartile range (IQR)0.005

Descriptive statistics

Standard deviation0.002946069133
Coefficient of variation (CV)0.6747110822
Kurtosis-0.6350712284
Mean0.004366415804
Median Absolute Deviation (MAD)0.002
Skewness0.1593145461
Sum9.283
Variance8.679323336 × 106
MonotocityNot monotonic
2021-01-28T00:15:43.203227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0332
15.6%
0.005290
13.6%
0.004244
11.5%
0.006231
10.9%
0.007216
10.2%
0.003212
10.0%
0.008160
7.5%
0.002160
7.5%
0.001118
 
5.6%
0.00982
 
3.9%
Other values (6)81
 
3.8%
ValueCountFrequency (%)
0332
15.6%
0.001118
 
5.6%
0.002160
7.5%
0.003212
10.0%
0.004244
11.5%
ValueCountFrequency (%)
0.0151
 
< 0.1%
0.0142
 
0.1%
0.0132
 
0.1%
0.01211
0.5%
0.01116
0.8%

light_decelerations
Real number (ℝ≥0)

ZEROS

Distinct16
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001889463782
Minimum0
Maximum0.015
Zeros1231
Zeros (%)57.9%
Memory size16.7 KiB
2021-01-28T00:15:43.351262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.003
95-th percentile0.008
Maximum0.015
Range0.015
Interquartile range (IQR)0.003

Descriptive statistics

Standard deviation0.002960208577
Coefficient of variation (CV)1.566692416
Kurtosis2.517460858
Mean0.001889463782
Median Absolute Deviation (MAD)0
Skewness1.718436858
Sum4.017
Variance8.762834818 × 106
MonotocityNot monotonic
2021-01-28T00:15:43.479290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
01231
57.9%
0.001163
 
7.7%
0.003118
 
5.6%
0.002115
 
5.4%
0.004114
 
5.4%
0.005107
 
5.0%
0.00674
 
3.5%
0.00855
 
2.6%
0.00754
 
2.5%
0.00937
 
1.7%
Other values (6)58
 
2.7%
ValueCountFrequency (%)
01231
57.9%
0.001163
 
7.7%
0.002115
 
5.4%
0.003118
 
5.6%
0.004114
 
5.4%
ValueCountFrequency (%)
0.0153
 
0.1%
0.0147
0.3%
0.0138
0.4%
0.01212
0.6%
0.01113
0.6%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0.0
2119 
0.001
 
7

Length

Max length5
Median length3
Mean length3.006585136
Min length3

Characters and Unicode

Total characters6392
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.02119
99.7%
0.0017
 
0.3%
2021-01-28T00:15:43.782349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-01-28T00:15:43.899376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.02119
99.7%
0.0017
 
0.3%

Most occurring characters

ValueCountFrequency (%)
04259
66.6%
.2126
33.3%
17
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4266
66.7%
Other Punctuation2126
33.3%

Most frequent character per category

ValueCountFrequency (%)
04259
99.8%
17
 
0.2%
ValueCountFrequency (%)
.2126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common6392
100.0%

Most frequent character per script

ValueCountFrequency (%)
04259
66.6%
.2126
33.3%
17
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII6392
100.0%

Most frequent character per block

ValueCountFrequency (%)
04259
66.6%
.2126
33.3%
17
 
0.1%

prolongued_decelerations
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0001585136406
Minimum0
Maximum0.005
Zeros1948
Zeros (%)91.6%
Memory size16.7 KiB
2021-01-28T00:15:43.983403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.002
Maximum0.005
Range0.005
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0005899475164
Coefficient of variation (CV)3.721746053
Kurtosis20.5159178
Mean0.0001585136406
Median Absolute Deviation (MAD)0
Skewness4.323965111
Sum0.337
Variance3.48038072 × 107
MonotocityNot monotonic
2021-01-28T00:15:44.117434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
01948
91.6%
0.00272
 
3.4%
0.00170
 
3.3%
0.00324
 
1.1%
0.0049
 
0.4%
0.0053
 
0.1%
ValueCountFrequency (%)
01948
91.6%
0.00170
 
3.3%
0.00272
 
3.4%
0.00324
 
1.1%
0.0049
 
0.4%
ValueCountFrequency (%)
0.0053
 
0.1%
0.0049
 
0.4%
0.00324
 
1.1%
0.00272
3.4%
0.00170
3.3%

abnormal_short_term_variability
Real number (ℝ≥0)

Distinct75
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.9901223
Minimum12
Maximum87
Zeros0
Zeros (%)0.0%
Memory size16.7 KiB
2021-01-28T00:15:44.275461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile21
Q132
median49
Q361
95-th percentile75
Maximum87
Range75
Interquartile range (IQR)29

Descriptive statistics

Standard deviation17.19281372
Coefficient of variation (CV)0.3658814423
Kurtosis-1.051029573
Mean46.9901223
Median Absolute Deviation (MAD)14
Skewness-0.01182857622
Sum99901
Variance295.5928436
MonotocityNot monotonic
2021-01-28T00:15:44.444508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6062
 
2.9%
5861
 
2.9%
6560
 
2.8%
6358
 
2.7%
6458
 
2.7%
6157
 
2.7%
5154
 
2.5%
6251
 
2.4%
2248
 
2.3%
2546
 
2.2%
Other values (65)1571
73.9%
ValueCountFrequency (%)
122
 
0.1%
137
0.3%
144
 
0.2%
154
 
0.2%
1612
0.6%
ValueCountFrequency (%)
871
 
< 0.1%
864
0.2%
846
0.3%
834
0.2%
822
 
0.1%
Distinct57
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.332784572
Minimum0.2
Maximum7
Zeros0
Zeros (%)0.0%
Memory size16.7 KiB
2021-01-28T00:15:44.627549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.3
Q10.7
median1.2
Q31.7
95-th percentile3
Maximum7
Range6.8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8832413341
Coefficient of variation (CV)0.6627037502
Kurtosis4.700756342
Mean1.332784572
Median Absolute Deviation (MAD)0.5
Skewness1.657339204
Sum2833.5
Variance0.7801152543
MonotocityNot monotonic
2021-01-28T00:15:44.786585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8125
 
5.9%
0.5121
 
5.7%
1.3121
 
5.7%
0.4120
 
5.6%
0.7117
 
5.5%
0.9114
 
5.4%
0.6113
 
5.3%
1.2107
 
5.0%
1.5100
 
4.7%
199
 
4.7%
Other values (47)989
46.5%
ValueCountFrequency (%)
0.247
 
2.2%
0.384
4.0%
0.4120
5.6%
0.5121
5.7%
0.6113
5.3%
ValueCountFrequency (%)
71
< 0.1%
6.91
< 0.1%
6.32
0.1%
61
< 0.1%
5.91
< 0.1%
Distinct87
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.846660395
Minimum0
Maximum91
Zeros1240
Zeros (%)58.3%
Memory size16.7 KiB
2021-01-28T00:15:44.961625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311
95-th percentile56
Maximum91
Range91
Interquartile range (IQR)11

Descriptive statistics

Standard deviation18.39687968
Coefficient of variation (CV)1.868336973
Kurtosis4.252997853
Mean9.846660395
Median Absolute Deviation (MAD)0
Skewness2.195075309
Sum20934
Variance338.4451818
MonotocityNot monotonic
2021-01-28T00:15:45.124662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01240
58.3%
152
 
2.4%
245
 
2.1%
543
 
2.0%
440
 
1.9%
336
 
1.7%
834
 
1.6%
631
 
1.5%
1229
 
1.4%
723
 
1.1%
Other values (77)553
26.0%
ValueCountFrequency (%)
01240
58.3%
152
 
2.4%
245
 
2.1%
336
 
1.7%
440
 
1.9%
ValueCountFrequency (%)
914
0.2%
902
0.1%
881
 
< 0.1%
861
 
< 0.1%
851
 
< 0.1%

mean_value_of_long_term_variability
Real number (ℝ≥0)

ZEROS

Distinct249
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.187629351
Minimum0
Maximum50.7
Zeros137
Zeros (%)6.4%
Memory size16.7 KiB
2021-01-28T00:15:45.288690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.6
median7.4
Q310.8
95-th percentile18.475
Maximum50.7
Range50.7
Interquartile range (IQR)6.2

Descriptive statistics

Standard deviation5.628246604
Coefficient of variation (CV)0.6874085725
Kurtosis4.131253848
Mean8.187629351
Median Absolute Deviation (MAD)3.1
Skewness1.331997908
Sum17406.9
Variance31.67715984
MonotocityNot monotonic
2021-01-28T00:15:45.469731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0137
 
6.4%
6.729
 
1.4%
7.129
 
1.4%
5.225
 
1.2%
6.525
 
1.2%
9.524
 
1.1%
8.523
 
1.1%
7.223
 
1.1%
5.623
 
1.1%
6.823
 
1.1%
Other values (239)1765
83.0%
ValueCountFrequency (%)
0137
6.4%
0.14
 
0.2%
0.24
 
0.2%
0.39
 
0.4%
0.46
 
0.3%
ValueCountFrequency (%)
50.71
< 0.1%
41.81
< 0.1%
40.81
< 0.1%
36.91
< 0.1%
35.71
< 0.1%

histogram_width
Real number (ℝ≥0)

Distinct154
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.44590781
Minimum3
Maximum180
Zeros0
Zeros (%)0.0%
Memory size16.7 KiB
2021-01-28T00:15:45.647771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile16
Q137
median67.5
Q3100
95-th percentile138
Maximum180
Range177
Interquartile range (IQR)63

Descriptive statistics

Standard deviation38.95569296
Coefficient of variation (CV)0.5529873086
Kurtosis-0.9022867793
Mean70.44590781
Median Absolute Deviation (MAD)31.5
Skewness0.3142347526
Sum149768
Variance1517.546014
MonotocityNot monotonic
2021-01-28T00:15:45.809808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3942
 
2.0%
10235
 
1.6%
2730
 
1.4%
3129
 
1.4%
9828
 
1.3%
9028
 
1.3%
9627
 
1.3%
2227
 
1.3%
8327
 
1.3%
4226
 
1.2%
Other values (144)1827
85.9%
ValueCountFrequency (%)
32
 
0.1%
52
 
0.1%
61
 
< 0.1%
73
 
0.1%
810
0.5%
ValueCountFrequency (%)
1801
 
< 0.1%
1766
0.3%
1632
 
0.1%
1621
 
< 0.1%
1615
0.2%

histogram_min
Real number (ℝ≥0)

Distinct109
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.579492
Minimum50
Maximum159
Zeros0
Zeros (%)0.0%
Memory size16.7 KiB
2021-01-28T00:15:45.988848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile51
Q167
median93
Q3120
95-th percentile139
Maximum159
Range109
Interquartile range (IQR)53

Descriptive statistics

Standard deviation29.56021226
Coefficient of variation (CV)0.3158834444
Kurtosis-1.290422198
Mean93.579492
Median Absolute Deviation (MAD)27
Skewness0.1157840211
Sum198950
Variance873.8061486
MonotocityNot monotonic
2021-01-28T00:15:46.167897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5077
 
3.6%
5250
 
2.4%
7149
 
2.3%
12048
 
2.3%
6045
 
2.1%
6843
 
2.0%
6741
 
1.9%
10340
 
1.9%
5136
 
1.7%
6235
 
1.6%
Other values (99)1662
78.2%
ValueCountFrequency (%)
5077
3.6%
5136
1.7%
5250
2.4%
5332
1.5%
5427
 
1.3%
ValueCountFrequency (%)
1591
 
< 0.1%
1581
 
< 0.1%
1561
 
< 0.1%
1552
0.1%
1543
0.1%

histogram_max
Real number (ℝ≥0)

Distinct86
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.0253998
Minimum122
Maximum238
Zeros0
Zeros (%)0.0%
Memory size16.7 KiB
2021-01-28T00:15:46.336936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum122
5-th percentile138
Q1152
median162
Q3174
95-th percentile198
Maximum238
Range116
Interquartile range (IQR)22

Descriptive statistics

Standard deviation17.94418311
Coefficient of variation (CV)0.1093988073
Kurtosis0.6327694777
Mean164.0253998
Median Absolute Deviation (MAD)11
Skewness0.5778624482
Sum348718
Variance321.9937075
MonotocityNot monotonic
2021-01-28T00:15:46.521969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15771
 
3.3%
17166
 
3.1%
15862
 
2.9%
15660
 
2.8%
15958
 
2.7%
15254
 
2.5%
15452
 
2.4%
17852
 
2.4%
17248
 
2.3%
16548
 
2.3%
Other values (76)1555
73.1%
ValueCountFrequency (%)
1222
 
0.1%
1232
 
0.1%
1253
0.1%
1265
0.2%
1272
 
0.1%
ValueCountFrequency (%)
2386
0.3%
2303
0.1%
2285
0.2%
2131
 
< 0.1%
2115
0.2%

histogram_number_of_peaks
Real number (ℝ≥0)

ZEROS

Distinct18
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.068203198
Minimum0
Maximum18
Zeros107
Zeros (%)5.0%
Memory size16.7 KiB
2021-01-28T00:15:46.687006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q12
median3
Q36
95-th percentile10
Maximum18
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.949385622
Coefficient of variation (CV)0.7249848343
Kurtosis0.5042105262
Mean4.068203198
Median Absolute Deviation (MAD)2
Skewness0.8928859139
Sum8649
Variance8.698875546
MonotocityNot monotonic
2021-01-28T00:15:46.825037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1357
16.8%
2331
15.6%
3269
12.7%
4258
12.1%
5210
9.9%
6158
7.4%
7145
6.8%
0107
 
5.0%
8106
 
5.0%
967
 
3.2%
Other values (8)118
 
5.6%
ValueCountFrequency (%)
0107
 
5.0%
1357
16.8%
2331
15.6%
3269
12.7%
4258
12.1%
ValueCountFrequency (%)
181
 
< 0.1%
162
 
0.1%
151
 
< 0.1%
145
0.2%
1310
0.5%

histogram_number_of_zeroes
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3236124177
Minimum0
Maximum10
Zeros1624
Zeros (%)76.4%
Memory size16.7 KiB
2021-01-28T00:15:46.980072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7060593732
Coefficient of variation (CV)2.181805563
Kurtosis30.36508416
Mean0.3236124177
Median Absolute Deviation (MAD)0
Skewness3.920287371
Sum688
Variance0.4985198384
MonotocityNot monotonic
2021-01-28T00:15:47.119104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
01624
76.4%
1366
 
17.2%
2108
 
5.1%
321
 
1.0%
52
 
0.1%
42
 
0.1%
71
 
< 0.1%
101
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
01624
76.4%
1366
 
17.2%
2108
 
5.1%
321
 
1.0%
42
 
0.1%
ValueCountFrequency (%)
101
< 0.1%
81
< 0.1%
71
< 0.1%
52
0.1%
42
0.1%

histogram_mode
Real number (ℝ≥0)

HIGH CORRELATION

Distinct88
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.4520226
Minimum60
Maximum187
Zeros0
Zeros (%)0.0%
Memory size16.7 KiB
2021-01-28T00:15:47.275139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile111.25
Q1129
median139
Q3148
95-th percentile160
Maximum187
Range127
Interquartile range (IQR)19

Descriptive statistics

Standard deviation16.38128927
Coefficient of variation (CV)0.1191782337
Kurtosis3.009530513
Mean137.4520226
Median Absolute Deviation (MAD)10
Skewness-0.9951778449
Sum292223
Variance268.3466383
MonotocityNot monotonic
2021-01-28T00:15:47.463181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133140
 
6.6%
15089
 
4.2%
13689
 
4.2%
14287
 
4.1%
14879
 
3.7%
14478
 
3.7%
12976
 
3.6%
14371
 
3.3%
12567
 
3.2%
12666
 
3.1%
Other values (78)1284
60.4%
ValueCountFrequency (%)
606
0.3%
675
0.2%
691
 
< 0.1%
711
 
< 0.1%
756
0.3%
ValueCountFrequency (%)
1871
 
< 0.1%
1866
0.3%
1804
0.2%
1791
 
< 0.1%
1766
0.3%

histogram_mean
Real number (ℝ≥0)

HIGH CORRELATION

Distinct103
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.6105362
Minimum73
Maximum182
Zeros0
Zeros (%)0.0%
Memory size16.7 KiB
2021-01-28T00:15:47.656225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum73
5-th percentile108
Q1125
median136
Q3145
95-th percentile157
Maximum182
Range109
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.59359633
Coefficient of variation (CV)0.1158423164
Kurtosis0.9334274874
Mean134.6105362
Median Absolute Deviation (MAD)10
Skewness-0.6510192413
Sum286182
Variance243.1602466
MonotocityNot monotonic
2021-01-28T00:15:47.840267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14365
 
3.1%
14464
 
3.0%
13563
 
3.0%
14161
 
2.9%
14060
 
2.8%
13259
 
2.8%
13358
 
2.7%
14558
 
2.7%
14757
 
2.7%
13657
 
2.7%
Other values (93)1524
71.7%
ValueCountFrequency (%)
731
< 0.1%
751
< 0.1%
761
< 0.1%
781
< 0.1%
791
< 0.1%
ValueCountFrequency (%)
1821
< 0.1%
1801
< 0.1%
1781
< 0.1%
1751
< 0.1%
1732
0.1%

histogram_median
Real number (ℝ≥0)

HIGH CORRELATION

Distinct95
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.0903104
Minimum77
Maximum186
Zeros0
Zeros (%)0.0%
Memory size16.7 KiB
2021-01-28T00:15:48.031310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum77
5-th percentile113
Q1129
median139
Q3148
95-th percentile159
Maximum186
Range109
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.46658886
Coefficient of variation (CV)0.1047617954
Kurtosis0.6672593255
Mean138.0903104
Median Absolute Deviation (MAD)10
Skewness-0.478414198
Sum293580
Variance209.2821931
MonotocityNot monotonic
2021-01-28T00:15:48.214352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14669
 
3.2%
14268
 
3.2%
13768
 
3.2%
14567
 
3.2%
14765
 
3.1%
14163
 
3.0%
15163
 
3.0%
13462
 
2.9%
14960
 
2.8%
14356
 
2.6%
Other values (85)1485
69.8%
ValueCountFrequency (%)
771
< 0.1%
781
< 0.1%
792
0.1%
821
< 0.1%
861
< 0.1%
ValueCountFrequency (%)
1861
< 0.1%
1831
< 0.1%
1801
< 0.1%
1781
< 0.1%
1771
< 0.1%

histogram_variance
Real number (ℝ≥0)

ZEROS

Distinct133
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.80809031
Minimum0
Maximum269
Zeros187
Zeros (%)8.8%
Memory size16.7 KiB
2021-01-28T00:15:48.393392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q324
95-th percentile76
Maximum269
Range269
Interquartile range (IQR)22

Descriptive statistics

Standard deviation28.97763601
Coefficient of variation (CV)1.540700599
Kurtosis15.13158926
Mean18.80809031
Median Absolute Deviation (MAD)6
Skewness3.219973835
Sum39986
Variance839.7033886
MonotocityNot monotonic
2021-01-28T00:15:48.574433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1248
 
11.7%
0187
 
8.8%
2166
 
7.8%
3161
 
7.6%
4108
 
5.1%
585
 
4.0%
874
 
3.5%
665
 
3.1%
753
 
2.5%
949
 
2.3%
Other values (123)930
43.7%
ValueCountFrequency (%)
0187
8.8%
1248
11.7%
2166
7.8%
3161
7.6%
4108
5.1%
ValueCountFrequency (%)
2691
< 0.1%
2541
< 0.1%
2501
< 0.1%
2431
< 0.1%
2411
< 0.1%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0
1115 
1
846 
-1
165 

Length

Max length2
Median length1
Mean length1.077610536
Min length1

Characters and Unicode

Total characters2291
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1
ValueCountFrequency (%)
01115
52.4%
1846
39.8%
-1165
 
7.8%
2021-01-28T00:15:48.892505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-01-28T00:15:49.001530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
01115
52.4%
11011
47.6%

Most occurring characters

ValueCountFrequency (%)
01115
48.7%
11011
44.1%
-165
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2126
92.8%
Dash Punctuation165
 
7.2%

Most frequent character per category

ValueCountFrequency (%)
01115
52.4%
11011
47.6%
ValueCountFrequency (%)
-165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2291
100.0%

Most frequent character per script

ValueCountFrequency (%)
01115
48.7%
11011
44.1%
-165
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2291
100.0%

Most frequent character per block

ValueCountFrequency (%)
01115
48.7%
11011
44.1%
-165
 
7.2%

fetal_health
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
1
1655 
2
295 
3
176 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2126
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
11655
77.8%
2295
 
13.9%
3176
 
8.3%
2021-01-28T00:15:49.304598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-01-28T00:15:49.404620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
11655
77.8%
2295
 
13.9%
3176
 
8.3%

Most occurring characters

ValueCountFrequency (%)
11655
77.8%
2295
 
13.9%
3176
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2126
100.0%

Most frequent character per category

ValueCountFrequency (%)
11655
77.8%
2295
 
13.9%
3176
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common2126
100.0%

Most frequent character per script

ValueCountFrequency (%)
11655
77.8%
2295
 
13.9%
3176
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2126
100.0%

Most frequent character per block

ValueCountFrequency (%)
11655
77.8%
2295
 
13.9%
3176
 
8.3%

Interactions

2021-01-28T00:14:41.803798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:41.985839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:42.142875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:42.312922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:42.481952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:42.641987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:42.815026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:42.985065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:43.141109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:43.304137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:43.469183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:43.625210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:43.787247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:43.960286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:44.117321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:44.277357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:44.433392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:44.590428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:44.747472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:44.919503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:45.091541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:45.275592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:45.571659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:45.746698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:45.934732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:46.116773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:46.287812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:46.461851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:46.645893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:46.819932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:46.999973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:47.187015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:47.359054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:47.535102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:47.706141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:47.879171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:48.053211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:48.213247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:48.384286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:48.554324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:48.724365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:48.884399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:49.058447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:49.225485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:49.381520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:49.541556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:49.708594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:49.865621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:50.026657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:50.195704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:50.352740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:50.514768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:50.671803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:50.828839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:50.986883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:51.157921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:51.342963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:51.515994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:51.701035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:51.877582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:52.066624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:52.250172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:14:52.564243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-01-28T00:15:27.720718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:27.883755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:28.058794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:28.221831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:28.378867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:28.535903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:28.691938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:28.854975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:29.031014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:29.197052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:29.373092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:29.548131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:29.714169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:29.894210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:30.068249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:30.230286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:30.398324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:30.576364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:30.742402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:30.912440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:31.086479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:31.247524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:31.408561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:31.569588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:31.731625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:31.888660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:32.057698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:32.214734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:32.382772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:32.551811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:32.709846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:32.881885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:33.047922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:33.203958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:33.361994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:33.526039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:33.681066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:33.842103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:34.010140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:34.165175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:34.327212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:34.483247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:34.656286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:34.816323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:34.994363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:35.156399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:35.334440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:35.510479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:35.671516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:35.843554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:36.009592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:36.164627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:36.321671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:36.484708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:36.640735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:36.799771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:36.968810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:37.123844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:37.284881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:37.440916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:37.597951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:37.752987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:37.923025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:38.079060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:38.246107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:38.415145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:38.573172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:38.745211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:38.910256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:39.066283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:39.222319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:39.386356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:39.540390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:40.044504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:40.214543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:40.369578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:40.528614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-28T00:15:40.682649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-01-28T00:15:49.546653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-01-28T00:15:50.038764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-01-28T00:15:50.524874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-01-28T00:15:51.021986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-01-28T00:15:51.443081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-01-28T00:15:41.020725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-01-28T00:15:41.740888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

baseline valueaccelerationsfetal_movementuterine_contractionslight_decelerationssevere_decelerationsprolongued_decelerationsabnormal_short_term_variabilitymean_value_of_short_term_variabilitypercentage_of_time_with_abnormal_long_term_variabilitymean_value_of_long_term_variabilityhistogram_widthhistogram_minhistogram_maxhistogram_number_of_peakshistogram_number_of_zeroeshistogram_modehistogram_meanhistogram_medianhistogram_variancehistogram_tendencyfetal_health
01200.0000.00.0000.0000.00.000730.5432.46462126201201371217312
11320.0060.00.0060.0030.00.000172.1010.413068198611411361401201
21330.0030.00.0080.0030.00.000162.1013.413068198511411351381301
31340.0030.00.0080.0030.00.000162.4023.0117531701101371341371311
41320.0070.00.0080.0000.00.000162.4019.911753170901371361381111
51340.0010.00.0100.0090.00.002265.900.015050200537610710717003
61340.0010.00.0130.0080.00.003296.300.015050200637110710621503
71220.0000.00.0000.0000.00.000830.5615.6686213000122122123313
81220.0000.00.0020.0000.00.000840.5513.6686213000122122123313
91220.0000.00.0030.0000.00.000860.3610.6686213010122122123113

Last rows

baseline valueaccelerationsfetal_movementuterine_contractionslight_decelerationssevere_decelerationsprolongued_decelerationsabnormal_short_term_variabilitymean_value_of_short_term_variabilitypercentage_of_time_with_abnormal_long_term_variabilitymean_value_of_long_term_variabilityhistogram_widthhistogram_minhistogram_maxhistogram_number_of_peakshistogram_number_of_zeroeshistogram_modehistogram_meanhistogram_medianhistogram_variancehistogram_tendencyfetal_health
21161400.0040.0000.0040.0000.00.0800.2362.21814015810147148149101
21171400.0000.0000.0080.0000.00.0790.3208.52612415010144143145111
21181400.0000.0000.0060.0010.00.0790.5267.02112915010145142145211
21191400.0000.0000.0070.0010.00.0790.6276.42612415010144141145111
21201400.0000.0000.0050.0010.00.0770.7176.03112415520145143145201
21211400.0000.0000.0070.0000.00.0790.2257.24013717740153150152202
21221400.0010.0000.0070.0000.00.0780.4227.16610316960152148151312
21231400.0010.0000.0070.0000.00.0790.4206.16710317050153148152412
21241400.0010.0000.0060.0000.00.0780.4277.06610316960152147151412
21251420.0020.0020.0080.0000.00.0740.4365.04211715921145143145101

Duplicate rows

Most frequent

baseline valueaccelerationsfetal_movementuterine_contractionslight_decelerationssevere_decelerationsprolongued_decelerationsabnormal_short_term_variabilitymean_value_of_short_term_variabilitypercentage_of_time_with_abnormal_long_term_variabilitymean_value_of_long_term_variabilityhistogram_widthhistogram_minhistogram_maxhistogram_number_of_peakshistogram_number_of_zeroeshistogram_modehistogram_meanhistogram_medianhistogram_variancehistogram_tendencyfetal_healthcount
01220.0000.0000.0000.00.00.0191.9015.139103142101201201223014
11230.0000.0000.0000.00.00.0490.8713.87463137201291271292112
21230.0030.0030.0000.00.00.0520.8215.49050140701291281304112
31230.0030.0040.0000.00.00.0500.9414.88258140701291281305112
41350.0000.0000.0000.00.00.0620.5716.99771168301431421441132
51380.0020.0000.0040.00.00.0410.8810.351105156401421421432112
61400.0070.0000.0040.00.00.0341.2010.360119179201561531555012
71440.0000.0190.0000.00.00.0760.46110.68171152301451441462122
81450.0000.0200.0000.00.00.0770.2455.821129150101461451470122
91460.0000.0000.0030.00.00.0650.4397.019137156101501491511122